Classification and Phenological Stage Monitoring of Grape Crop using Sentinel-1 and Sentinel-2 Time Series and Deep Learning Techniques

Abstract

Abstract. The demand for food production is increasing rapidly with a surge in the population. To cope with this increasing food demand, precise agricultural management is essential. The existing techniques involve extensive field surveys for agricultural land discrimination. To minimize the man-hour efforts and time required by these techniques, automated techniques for precise crop type mapping and monitoring have been used. These techniques utilize satellite imagery and advanced machine learning techniques for crop type mapping and monitoring. However, the performance of such techniques is affected by factors such as fragmented land parcels, seasonal variability, and inconsistent field-level observations. To overcome these issues, this study attempts to classify grape and non-grape crops and monitor their phenological stages in the study area in Pune district, India, using Sentinel-2 satellite imagery and deep learning (DL) segmentation techniques: U-Net and DeepLabV3. Further, Sentinel- 1C SAR imagery (VV and VH polarization) for the years 2016 to 2024 was utilized to train and evaluate a long short-term memory network (LSTM) model with an aim to analyze the temporal behavior of the grape crop from pruning to harvesting stage with emphasis on growth stages like leaf set, fruit set, and ripening. The experimental results demonstrate that U-Net outperforms DeepLabV3 (F1-score: 0.96; mAP: 0.95) in grape crop classification. The LSTM model showed performance (F1-score 0.82) for phenological stage identification. This study can help agricultural stakeholders in effective and large-scale crop discrimination with minimum human intervention. It has the potential to reveal grape distribution and development stages in a faster time.

Plant phenotyping relevance

衛星画像と深層学習を用いてブドウ作物の分類および生育（フェノロジー）段階を推定し、モデル性能も評価しているため、植物状態の取得・抽出手法が中心である。

Code and data availability

The paper describes Sentinel-1/2 imagery, digitised training polygons, and U-Net/DeepLabV3/LSTM models, but contains no public dataset deposit, no author code release, and no availability statement or URL for any paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.